Face recognition based on convolution neural network

Yan, Kewen; Huang, S.; Song, Yaoxian; Liu, Wei; Fan, Neng

doi:10.23919/chicc.2017.8027997

Cited by 64 publications

(17 citation statements)

References 12 publications

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“…YOLOv2 with anchor boxes predicts around 1000 boxes per image whereas YOLO predicts only 98 boxes per image. Without anchor boxes, YOLO gets a recall of 81% with 69.5% map but with anchor boxes, YOLOv2 gets a recall of 88% with 69.2% map [23].…”

Section: Yolo Vs Yolov2mentioning

confidence: 99%

Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

2019

ijrte

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Object detection has boomed in areas like image processing in accordance with the unparalleled development of CNN (Convolutional Neural Networks) over the last decade. The CNN family which includes R-CNN has advanced to much faster versions like Fast-RCNN which have mean average precision(Map) of up to 76.4 but their frames per second(fps) still remain between 5 to 18 and that is comparatively moderate to problem-solving time. Therefore, there is an urgent need to increase speed in the advancements of object detection. In accordance with the broad initiation of CNN and its features, this paper discusses YOLO (You only look once), a strong representative of CNN which comes up with an entirely different method of interpreting the task of detecting the objects. YOLO has attained fast speeds with fps of 155 and map of about 78.6, thereby surpassing the performances of other CNN versions appreciably. Furthermore, in comparison with the latest advancements, YOLOv2 attains an outstanding trade-off between accuracy and speed and also as a detector possessing powerful generalization capabilities of representing an entire image

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Section: Yolo Vs Yolov2mentioning

confidence: 99%

Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

2019

ijrte

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“…But initially, we need to detect the face in the image in order to perform mask detection. For this, we have used Caffe-based face detector [17], which is available in the face detector subdirectory of Deep Neural Network samples. We set a parameter called confidence which is a selectable probability threshold that can be fixed to override 50 % for filtering weak face detections.…”

Section: Perform Face Detectionmentioning

confidence: 99%

An Efficient Face Mask Detector with PyTorch and Deep Learning

Basha¹,

Pravallika²,

Shankar³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The outbreak ofacoronavirus disease in 2019 (COVID-19) has created a global health epidemic that has had a major effect on the way we view our environment and our daily lives. The Covid-19 affected numbers are rising at a tremendous pace. Because of that, many countries face an economic catastrophe, recession, and much more. One thing we should do is to separate ourselves from society, remain at home, and detach ourselves from the outside world. But that's no longer a choice, people need to earn to survive, and nobody can remain indefinitely within their homes. As a precaution, people should wear masks while keeping social distance, but some ignore such things and walk around.OBJECTIVES: To develop aFace Mask Detector with OpenCV, PyTorch, and Deep Learning that helps to detect whether or not a person wears a mask.METHODS: A Neural Network model called ResNet is trained on the dataset. Furthermore, this work makes use of the inbuilt Face Detector after training. Finally, we predict whether or not a person is wearing a mask along with the percentage of the face covered or uncovered.RESULTS: The validation results have been proposed to be 97% accurate when compared to applying different algorithms.CONCLUSION: This Face Mask Detection System was found to be apt for detecting whether or not people wear masks in public places which contribute to their health and also to the health of their contacts in this COVID-19 pandemic.

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“…The output feature maps obtained after the calculation of the ConvL are generally not much reduced in dimension. If the dimension does not change, there will be a great amount of computation need to do, and the network learning process will become very difficult, more likely to get a reasonable result [26]. The pooling layer is another important concept of CNN's simplifies the output by performing non-linear downsampling, reducing the number of parameters that the network needs to learn, and don't change the number of feature graphs; the pooling layer is sampled with the maximum value, the sampling size is 2x2.…”

Section: Maxpooling Layermentioning

confidence: 99%

Illumination-robust face recognition based on deep convolutional neural networks architectures

Bendjillali¹,

Beladgham²,

Merit³

et al. 2020

IJEECS

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<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classiﬁcation. For learning the features followed by classiﬁcation we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

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Face recognition based on convolution neural network

Cited by 64 publications

References 12 publications

Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

An Efficient Face Mask Detector with PyTorch and Deep Learning

Illumination-robust face recognition based on deep convolutional neural networks architectures

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